System and method of power-saving in mems sensor applications

ABSTRACT

At least some of the embodiments are methods including detecting low user dynamics by a first MEMS sensor, determining a first sensor sampling rate value corresponding to the low user dynamics wherein the first sensor sampling rate value is less than a second sensor sampling rate value corresponding to high user dynamics, and adjusting a sampling rate of a second MEMS sensor to the first sensor sampling rate value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of and claims priority toU.S. patent application Ser. No. 13/023,987 filed Feb. 9, 2011 titledAccelerometer-Aided Gyroscope which is hereby incorporated by referenceas if reproduced in full below.

TECHNICAL FIELD

The present invention relates to micro-electromechanical system (MEMS)sensors and, in particular to systems and methods for power savings inMEMS sensor applications.

BACKGROUND

Some navigation systems are based on signals received from orbitingsatellites, such as the Global Positioning System (GPS) satellites.Performance of such systems degrades if the receiver does not have adirect line-of-sight to the orbiting satellites. Performance of suchsatellite-based systems degrades if the signal from the satellite isblocked such as by buildings, mountains, etc. Using MEMS sensors toaugment such satellite-based navigation systems is a way to mitigateagainst signal degradation or loss in satellite-based navigationsystems.

MEMS sensors used in consumer electronics devices, smartphones or tabletcomputers, for example, typically have a tradeoff between sampling rateand power consumed by the sensor. Further, depending on the applicationof the sensor output, performance may be reflected in the sampling rate.Further, in handheld or other battery-powered device power budgets maybe constrained. Thus, any system or method that can reduce powerconsumption without noticeably degrading performance provides acompetitive advantage in the marketplace.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of exemplary embodiments of the invention,reference will now be made to the accompanying drawings in which:

FIG. 1 shows a block diagram of a system in accordance with anembodiment of the disclosure;

FIG. 2 shows a block diagram of a sensor in accordance with anembodiment of the disclosure;

FIG. 3 shows a block diagram of a sensor in accordance with anotherembodiment of the disclosure

FIG. 4 shows a block diagram of a system in accordance with anotherembodiment of the disclosure;

FIG. 5 shows a flowchart of a process in accordance with an embodimentof the disclosure;

FIG. 6 shows in further detail a flowchart of a portion of the processof FIG. 5;

FIG. 7 shows in further detail a flowchart of another portion of theprocess of FIG. 5;

FIG. 8 shows in further detail a flowchart of yet another portion of theprocess of FIG. 5;

FIG. 9 shows a flowchart of a process in accordance with anotherembodiment of the disclosure;

FIG. 10 shows a flowchart of a process in accordance with still anotherembodiment of the disclosure; and

FIG. 11 shows a flowchart of a process in accordance with still anotherembodiment of the disclosure.

NOTATION AND NOMENCLATURE

Certain terms are used throughout the following description and claimsto refer to particular system components. As one skilled in the art willappreciate, companies may refer to a component by different names. Thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . .” Also, the term “couple” or “couples” is intended tomean either an indirect, direct, optical or wireless electricalconnection. Thus, if a first device couples to a second device, thatconnection may be through a direct electrical connection, through anindirect electrical connection via other devices and connections,through an optical electrical connection, or through a wirelesselectrical connection.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of theinvention. Although one or more of these embodiments may be preferred,the embodiments disclosed should not be interpreted, or otherwise used,as limiting the scope of the disclosure, including the claims. Inaddition, one skilled in the art will understand that the followingdescription has broad application, and the discussion of any embodimentis meant only to be exemplary of that embodiment, and not intended tointimate that the scope of the disclosure, including the claims, islimited to that embodiment.

Refer now to FIG. 1 illustrating a block diagram of a system 100 foradaptively controlling MEMS sensor sampling rates in accordance with atleast some embodiments of the disclosure. System 100 may comprise aportion of an end-user device such as a Tablet computer, smartphone,vehicular navigation system or the like. The principles disclosed hereinare not limited to particular end-user devices.

System 100 includes a plurality of sensors 102 which may comprise, forexample, an accelerometer 102A, a magnetometer (or, equivalently,ecompass) 1028, a gyroscope 102C and altimeter 102D. One or more of theplurality of sensors 102 may provide signals indicative of therespective physical parameters to an application or hardware devicewithin an end-user device coupled to sensors 102. Sink 104 comprises anysuch hardware device or application that uses signals from at least oneof the plurality of sensors 102. System 100 also includes dynamicsestimation logic 106 which also receives output signals from at leastone of the plurality of sensors 102, sampling rate engine 108 whichreceives the output of dynamic estimation logic 106 to generate a sensorsampling rate for the sensor or sensors to be controlled and ratecontrol generator 110 which receives the sampling rate and generates asignal to feed back to the sensor or sensors under control. In thisrespect, in accordance with the principles herein, in at least someembodiments, the sensors being controlled need not be the same sensorsused to estimate dynamics. Further, the rate control generator may, inat least some embodiments, provide different sampling rates for eachsensor being controlled.

Considering first dynamics estimation logic 106, based on the outputsignals from the plurality of sensors, dynamics estimation logic 106determines if user dynamics associated with the end user device is highor low. In at least some embodiments, the determination may be madeusing the output of one of the plurality of sensors 102. Alternatively,in other embodiments the determination may be made using signals fromtwo or more sensors. For example, dynamics estimation logic 106 mayreceive acceleration value signals from accelerometer 102A. In someembodiments, accelerometer 102A comprises a single axis accelerometerwhile in other embodiments accelerometer 102A comprises a multi-axisaccelerometer. For example, the accelerometer in some embodimentscomprises a 3-axis accelerometer. Accordingly, in a single axisaccelerator embodiment, dynamics estimation logic 106 may determine theacceleration that the end-user device is experiencing along the singleaccelerometer axis. In at least some embodiments including a single-axisaccelerometer, if, for example, the acceleration value indicated by theaccelerometer is below a predetermined value, dynamics estimation logic106 may determine that the user dynamics are low, and signal samplingrate engine 108 accordingly. In yet other embodiments comprising asingle-axis accelerometer, dynamics estimation logic 106 may comprise aplurality of predetermined acceleration levels, and generate a signal tosampling rate engine 108 in accordance with a comparison of theacceleration values received from accelerometer 102A and each of theplurality of acceleration levels. In other words, as user dynamics,represented by acceleration values from the accelerometer fall belowpredetermined acceleration levels, dynamics estimation logic 106 signalssampling rate generator 108 accordingly, as described further below.Additionally, as previously described, in at least some embodiments,accelerometer 102A may comprise a multi-axis accelerometer whereindynamics estimation logic 106 receives an acceleration value for each ofthe axes of the accelerometer. In such embodiments, dynamics estimationlogic 106 may, in at least some embodiments, determine user dynamicsbased on the largest acceleration value of the components of theacceleration along each of the accelerometer axes by comparing thelargest component with a predetermined acceleration level, similar toembodiments using a single-axis accelerometer. Likewise, in embodimentsof dynamic estimation logic 106 comprising a plurality of predeterminedacceleration levels, the largest component of the acceleration from themulti-axis accelerometer may be compared with each of the plurality ofacceleration levels and the corresponding signal provided to samplingrate engine 108. Alternatively, in yet other embodiments, dynamicsestimation logic 106 may compute from the accelerations for each of theaxes, the magnitude of the acceleration representing the user dynamics.Recall that acceleration is a vector quantity and the accelerationvalues for each of the axes in a multi-axis accelerometer correspond tothe components of the acceleration experienced by the accelerometeralong the direction corresponding to each of the axes. The magnitude ofthe acceleration is a scalar value formed from the components ofacceleration vector. The computation of the magnitude of a vector fromthe values of the components thereof would be readily know to those ofordinary skill in the art. The magnitude of the acceleration may then beused, in at least some embodiments, by dynamics estimation logic 106 tocompare against a predetermined acceleration level in, or against aplurality of such levels, as in the single-axis embodiment describedabove. In addition to the magnitude of the acceleration, other metricsmay be used. Examples of such other metrics are described in the relatedU.S. patent application Ser. No. 13/023,987 for “ACCELEROMETER-AIDEDGYROSCOPE” which is hereby incorporated by reference herein as if setforth in its entirety. One such exemplary metric is the magnitude of theacceleration adjusted for the acceleration of gravity. Another exemplarymetric is the change in the sensor output (e.g. acceleration, angularrate, magnetic field intensity, pressure), if the device has lowdynamics then it will see less change in the sensor output (e.g. theconstant acceleration of gravity), but changes in the sensor outputindicate higher dynamics.

Similar to accelerometer 102A, dynamics estimation logic 106 may alsoreceive signals from magnetometer 102B, gyroscope 102C and altimeter102D that are indicative of user dynamics. For example, signals frommagnetometer 1028 may indicate that an end-user device is undergoing noor otherwise slow rotation. Dynamics estimation logic 106 may determine,for example, that the rotation rate signaled by the magnetometer isbelow a predetermined rotation rate value, and further signal samplingrate engine 108 accordingly. Similarly, dynamics estimation logic 106may compare rotation rates from magnetometer 102B with a plurality ofpredetermined rotation rate levels, and signal sampling rate engine 108to generate sampling rates based on the level just exceeding therotation rate obtained from the output of magnetometer 102B. Signalsfrom gyroscope 102C representative of the orientation of an end-userdevice such as a smartphone, vehicle navigation system may be similarlyused by dynamics estimation logic 106. In accordance with someembodiments, the gyroscope 102C comprises a single axis gyroscope while,in other embodiments, the gyroscope 102C comprises a multi-axisgyroscope. For example, the gyroscope 102C in some embodiments comprisesa 3-axis gyroscope. Signals provided by gyroscope 102C to dynamicsestimation logic 106 may be indicative of changes in orientation of anend-user device relative to the axes of the gyroscope. The rate ofchange of these signals may be determined by dynamics estimation logic106 which may then further be used to determine user dynamics, inparticular when user dynamics are low. For example, similarly to themagnitude of an acceleration vector, a magnitude of a rotation ratevector may be computed by dynamics estimation logic 106, and comparedagainst a predetermined rotation rate level, or alternatively, against aplurality of predetermined levels. Based on the outcome of thecomparison, dynamics estimation logic 106 signals sampling rate engine108 accordingly. Note that, in at least dome embodiments, the monitoringof user dynamics may be continuous and dynamics estimation logic 106 maysend signals to restore a sampling rate to a high value based onincreasing user dynamics. Further, as described in conjunction withFIGS. 5-8 below, a sensor sampling rate may be reduced for apredetermined length of time whereby dynamics estimation logic 106 maysend signals to restore a sampling rate after expiry of the timeinterval. Also, in at least some embodiments, dynamics estimation logic106 may include a hysteresis whereby, on detecting low user dynamics, adelay is introduced before dynamics estimation logic 106 sends a signalto reduce a sensor sampling rate. And, in at least some embodiments, asignal to reduce sampling rates may be indicative of a zero samplingrate; that is, a signal to turn off a particular sensor or,alternatively switch to a low-power mode.

Sampling rate engine 108 determines a sampling rate for one or more ofthe plurality of sensors 102 based on the signal indicative of userdynamics received from dynamics estimation logic 106. In at least someembodiments, the sampling rates may be different for each of the sensorscomprising the plurality of sensors 102. Further, in at least someembodiments, the choice of sampling rates may be binary, based on anestimate of user dynamics being above or below a predetermined level, asdiscussed above. In other embodiments, the sampling rate may beestablished with a finer granularity based on the estimate of thedynamics falling within a predetermined set of ranges of values, inaccordance with at least some embodiments as also described above.

Sampling rate engine 108, in at least some embodiments may determine asampling rate for each sensor of the plurality of sensors 102 based on amaximum sampling rate specification for the respective sensor and acorresponding signal indicative of user dynamics from dynamicsestimation logic 106. For example, in at least some embodiments,sampling rate engine 108 may scale the maximum sampling rate for asensor in proportion to the corresponding dynamics estimation levelcommunicated by the signal from dynamics estimation logic 106. Further,in at least some embodiments, sampling rate engine 108 may determine asampling rate for a particular sensor based on, for example, performinga calibration of the sensor or a requested override of any sampling ratereduction, as described further below.

The sampling rate from sampling rate engine 108 is input to rate controlgenerator 110. Rate control generator 110 maps the sampling rate to acontrol value appropriate to the sensor whose sampling rate is to be setin response to a change in user dynamics. For example, as previouslydescribed, in at least some embodiments, sampling rate control may bebinary, in which case, a sensor sampling rate may be switched between ahigh, or maximum value appropriate to high user dynamics, and a lowvalue suitable to low user dynamics. Such binary control may beunderstood by referring to FIG. 2 illustrating a block diagram of anexemplary sensor 202 configured to have two selectable sampling rates inaccordance with the principles of the disclosure. Sensor 202 mayrepresent any of sensors 102A-D. Sensor 202 comprises an analog section204 and two analog-to-digital converters, ADC 206A and ADC 206B. TheADCs receive an analog signal representative of a value of the physicalparameter sensed by sensor 202 (e.g. acceleration, orientation, magneticfield, altitude or barometric pressure) and convert the analog signal toa digital value which may then be used by sink 104. For the purpose ofillustration, let ADC 206A have a fast or high sampling rate, and ADC206B have a slow or low sampling rate. Those skilled in the art wouldrecognize that such identification is a matter of choice and does notreflect a limitation on an embodiment of sensor 202. ADCs 206A and 206Bmay be configured to be turned off or otherwise enter a quiescent statein which they consume little or no power via an ADC control signal 208.Control signal 208 may be selectively provided to the ADCs viade-multiplexer (DEMUX) 210. DEMUX 210 may be controlled via rate control212 which may be a binary signal from rate control generator 110. Forexample, the illustrative embodiment of FIG. 2 may be configured toselect ADC 206A via binary ‘0’ and ADC 206B via binary ‘1’ and theembodiment of rate control generator 110 configured accordingly.However, it would be recognized by those skilled in the art that theconfiguration is a design choice and not a limitation with respect tothe exemplary embodiments. Further, in at least some embodiments, ratecontrol generator 110 may set the control line to the appropriate binaryvalue even if the rate set by generator 108 is more granular. In such anembodiment, the rate control generator 110 may select the ADC, andthereby the sampling rate, by for example, thresholding the samplingrate received from sampling rate generator 108 against a predeterminedvalue as established by the specifications of sensor 202. In addition tocontrolling the selection of one of ADCs 206A and 206B, rate control 212may also control the steering of the ADC outputs to sink 104, viamultiplexer 214, in the exemplary embodiment of FIG. 2. Further, whiletwo selectable ADCs have been shown in the example embodiment in FIG. 2,in at least some other embodiments a sensor could include more than twosuch ADCs and thereby increase the granularity of the sampling rateselection. In at least some such embodiments, rate control 212 mightcomprise a multi-bit binary signal. Note also that while in the exampleembodiment of FIG. 2, ADC selection and ADC output signal switching hasbeen described in terms of multiplexers, other switching mechanisms maybe used and would be appreciated by those of ordinary skill in the art,and such mechanisms would fall within the spirit and scope of thepresent disclosure. Further, in at least some embodiments, a sensor mayinclude an ADC having a programmable sampling rate provided, forexample, by a programmable phase locked loop (PLL). Referring to FIG. 3,there is illustrated therein a block diagram sensor 302 in accordancewith such an exemplary embodiment. Sensor 302 may represent any ofsensors 102A-D. Sensor 302 comprises an analog section 304 and ADC 306.ADC 306 includes PLL 308 which is configured to receive a rate controlsignal 310 from rate control generator 110. In at least someembodiments, rate control signal 310 may comprise a multi-bit binaryvalue that programs taps on a programmable divider within PLL 308 (notshown in FIG. 3). The foregoing embodiments are intended to be exemplaryand other mechanisms for providing a programmable ADC sampling rateknown to those skilled in the art could also be used and would fallwithin the principles of the disclosure.

Refer now to FIG. 4 illustrating a block diagram of a system 400 foradaptively controlling MEMS sensor sampling rates in accordance with atleast some alternative embodiments of the disclosure. System 400included a plurality of sensors 102, comprising an accelerometer 102A, amagnetometer 102B, a gyro 102C and an altimeter 102D. Output data fromthe plurality of sensors 102 is coupled to a sensor processor 402.Sensor processor 402 may forward the data as received to sink 104,similar to system 100 in FIG. 1. Further, sensor processor 402 mayinclude dynamic estimation logic (not shown in FIG. 4) which operatesanalogously to dynamic estimation logic 106 in system 100. As would beappreciated by those skilled in the art, dynamic estimation logic may,in some embodiments be implemented in hardware within sensor processor402, and in other, alternative, embodiments implemented in software orfirmware executed by sensor processor 402. Further, an end user devicemay use the outputs of multiple sensors to determine a parameter ofinterest. For example, a navigation application or device may useaccelerometer, gyroscope and magnetometer outputs. In at least someembodiments, sensor processor 402 may be used to generate such parameterestimates. Exemplary processes which may be used to estimate particularparameters and which may, in at least some embodiments, be performed atleast in part by sensor processor 402 are described below in conjunctionwith FIGS. 8-9. Sensor processor 402 may, in at least some embodimentsbe implemented in hardware such as an application specific integratedcircuit (ASIC), or, in alternative embodiments, as software of firmwarerunning on a general purpose central processing unit (CPU) or a specialpurpose processor such as a graphics processing unit (GPU) (for example,using the enhanced floating-point capabilities of such processors).

To further understand the principles of the disclosure, refer now toFIG. 5 illustrating a flow chart of process 500 for adaptively adjustingsensor sampling rates in accordance with at least some embodiments. Atleast some portions of process 500 may be performed by sensor processor402. And, at least some portions of process 500 may be implementedhardware, such as an application specific integrated circuit (ASIC), oras software of firmware running on a general purpose central processingunit (CPU) or a special purpose processor such as a graphics processingunit (GPU) (for example, using the enhanced floating-point capabilitiesof such processors). Process 500 starts at block 510 and enters a loopvia “WHILE” block 512. In block 512, while user dynamics are determinedto be high, as for example detected by dynamics estimation logic 106 inan embodiment in accordance with system 100, or sensor processor 402 inan embodiment in accordance with system 400, process 500 loops throughthe “T” or “true” branch of block 512. On detection of low userdynamics, breaks out of the loop and proceeds via the “F” or “false”branch of block 512 to set or adjust gyro, magnetometer, and/oraccelerometer sampling rates in blocks 514, 516 and 517, respectively.Blocks 514, 516 and 517 are described further below. Returning to the“T” branch of block 512, while user dynamics are high, sampling ratesare held high, block 518. Additionally, in at least some embodiments, itmay be desirable to calibrate a magnetometer when sampling rates arehigh. And, in particular it may be desirable to calibrate a magnetometerwhen user dynamics are high whereby the three axes of the magnetometermay all be rotated. In this way, the calibration accuracy is benefitedby the high user dynamics and high sampling rate. Thus, in block 520,magnetometer calibration is enabled while high user dynamics are high.An alternative mechanism, which may be used when sensor calibrationduring low user dynamics is desirable, is discussed below in conjunctionwith the adjusting of gyro sampling rates, block 514, now considered.

Referring to FIG. 6, there is illustrated therein a process foradaptively adjusting a gyro sampling rate, block 514, in further detail.As would be understood by those skilled in the art, in at least someembodiments, all of portions of block 514 may be implemented inhardware, such as an application specific integrated circuit (ASIC) orin at least some alternative embodiments as software or firmware runningon, for example, sensor processor 402 or a general purpose centralprocessing unit (CPU) (not shown) in the device employing the pluralityof sensors 102.

In decision block 602, gyro sampling rate process 514 determines if thelow user dynamics detected in block 512 is overridden. If a device orapplication that uses output from the gyro, represented in exemplarysystems 100 and 400, by sink 104, requires gyro data at a high samplingrate regardless or independent of user dynamics. In at least someembodiments such a device or application may assert an override that maybe detected in decision block 602. If such an override is assertedprocess 514 returns to block 512. Even if low user dynamics persists, bylooping through the “F” branch of block 512 and blocks 514 and 602, thegyro sampling rate may be maintained until the device or applicationde-asserts its override.

If no override has been asserted or a previously asserted override isde-asserted, block 602 falls through the “No” branch, and in block 604,the gyro sampling rate is reduced. In at least some embodiments inaccordance with system 100, block 604 may be performed, at least inpart, by dynamics estimation logic 106, sampling rate engine 108 andrate control generator 110. Similarly, in at least some embodiments inaccordance with system 400, block 604 may be performed, at least inpart, by sensor processor 402, sampling rate engine 108 and rate controlgenerator 110.

In at least some embodiments, it may be desirable to maintain a reducedsampling rate for a predetermined or preselected time interval. Forexample, if one or more of the plurality of sensors is used to detectwhen the end-user device is stationary, the sampling rate may be reduced(or turned off) for a predetermined time interval and then restored tosense if the end-user device is still stationary. Thus, if for thepurpose of illustration, if an acceptable latency in sensing if thedevice is stationary is 1 second, the sensors might be turned off for900 milliseconds (ms) and then turned on to use the last 100 ms to senseif the device is still stationary. If such a time mode is provided,block 606 proceeds by the “Yes” branch to block 608. Otherwise, block606 proceeds to block 612, described below, via the “No” branch.Returning to decision block 608, it is determined if the aforementionedpredetermined or preselected time interval is expired. If yes, decisionblock 608 proceeds to block 610 and the sampling rate is restored to itspre-reduction value and gyro rate process 514 returns to block 512.Otherwise, if the time interval has not expired, decision block 608proceeds to block 612.

In at least some embodiments, blocks 612 and 614 provide for calibrationof a gyro during instances of low user dynamics. In some embodiments, itmay be desirable to calibrate a gyro sensor at low user dynamics.However, calibration may, in many, if not most embodiments benefit froma high sampling rate. The high number of samples provided therebyincrease calibration accuracy. Thus, in block 612, if a gyro sensor isbeing calibrated during an instance of low user dynamics, via the “T”branch, the gyro sampling rate is restored to a high value in block 614,and the high sampling rate is maintained while the calibration proceedsvia the loop in decision block 612. In at least some embodiments,calibration may proceed until user dynamics are no longer low. Whencalibration is complete (WHILE(calibrate)=FALSE), decision block 612returns to block 512 by the “F” branch. If upon return to block 512,user dynamics are low, block 512 proceeds via the “F” branch back toblock 514 where the gyro sampling rate may be reduced, reflecting thelow user dynamics via blocks 602 and 604.

Returning to FIG. 5, while user dynamics are low, block 512, in at leastsome embodiments the magnetometer sensor sampling rate may also beadjusted, block 516. FIG. 7 illustrates a process 516 for adaptivelyadjusting a magnetometer sampling rate in accordance with at least someembodiments of the disclosure.

In decision block 702 it is determined if a device or application thatuses output from the magnetometer, represented in exemplary systems 100and 400 by sink 104, requires magnetometer data at a high sampling ratehas asserted a signal to override reduction of the magnetometer samplingrate. Block 702 is analogous to block 602, FIG. 6 with respect to themagnetometer sampling rate. If such an override is asserted process 516returns to block 512, and the magnetometer sampling rate remains high.As described above in conjunction with block 602, even if low userdynamics persists, by looping through the “F” branch of block 512 andblocks 516 and 702, the magnetometer sampling rate may be maintaineduntil the device or application de-asserts its override.

If no override has been asserted or a previously asserted override isde-asserted, block 702 falls through the “No” branch, and in block 704,magnetometer calibration is disabled. Recall, as previously describedabove, in at least some embodiments, it may be desirable to calibrate amagnetometer when user dynamics are high. Block 520, FIG. 5 provides formagnetometer calibration when user dynamics are high. Moreover, as wouldbe appreciated by those skilled in the art, in alternative embodimentsin which magnetometer calibration may be performed or desired when userdynamics are low, a calibration mechanism paralleling blocks 612 and614, FIG. 6 may be used, and such embodiments would fall within theprinciples disclosed herein.

Continuing, in block 706, the magnetometer sampling rate is reduced. Inat least some embodiments in accordance with system 100, block 604 maybe performed, at least in part, by dynamics estimation logic 106,sampling rate engine 108 and rate control generator 110. Similarly, inat least some embodiments in accordance with system 400, block 706 maybe performed, at least in part, by sensor processor 402, sampling rateengine 108 and rate control generator 110.

Similar to process 514 with respect to adaptive adjustment of the gyrosampling rate, in at least some embodiments, process 516 may reduce themagnetometer sampling rate for a predetermined or preselected timeinterval. An example of such use has been described above in conjunctionwith FIG. 6. Thus, in block 708, it is determined if the sampling rateis to be reduced for a predetermined time interval (time mode). If not,process 516 returns to block 512 via the “No” branch and while userdynamics are low, the magnetometer sampling rate is maintained at a lowvalue via the “F” branch of block 512, block 516, the “No” branch ofblock 702 and block 706. If, however, in block 708 it is determined thatthe sampling rate is to be reduced for a predetermined time interval,block 710 loops until the predetermined time interval expires. On expiryof the time interval, block 710 breaks out of the loop via the “Yes”branch and in block 712, the sampling rate is restored to a high value.Process 516 then returns to block 512.

Returning to FIG. 5, while user dynamics are low, block 512, in at leastsome embodiments the accelerometer sensor sampling rate may also beadjusted, block 517. In at least some embodiments, the power consumptionof the accelerometer may be sufficiently low that it may be advantageousto omit adaptively reducing the accelerometer sampling rate. In otherembodiments, it may be desirable to adaptively reduce the sampling rateof the accelerometer. Referring to FIG. 8, there is illustrated thereina process for adaptively adjusting an accelerometer sampling rate, block517, in further detail. As would be understood by those skilled in theart, in at least some embodiments, all of portions of block 517 may beimplemented in hardware, such as an application specific integratedcircuit (ASIC) or in at least some alternative embodiments as softwareor firmware running on, for example, sensor processor 402 or a generalpurpose central processing unit (CPU) (not shown) in the deviceemploying the plurality of sensors 102.

In decision block 802, accelerometer sampling rate process 517determines if the low user dynamics detected in block 512 is overridden.If a device or application that uses output from the accelerometer,represented in exemplary systems 100 and 400, by sink 104, requiresaccelerometer data at a high sampling rate regardless or independent ofuser dynamics. In at least some embodiments such a device or applicationmay assert an override that may be detected in decision block 802. Ifsuch an override is asserted process 517 returns to block 512. Even iflow user dynamics persists, by looping through the “F” branch of block512 and blocks 517 and 802, the accelerometer sampling rate may bemaintained until the device or application de-asserts its override.

If no override has been asserted or a previously asserted override isde-asserted, block 802 falls through the “No” branch, and in block 804,the accelerometer sampling rate is reduced. In at least some embodimentsin accordance with system 100, block 804 may be performed, at least inpart, by dynamics estimation logic 106, sampling rate engine 108 andrate control generator 110. Similarly, in at least some embodiments inaccordance with system 400, block 804 may be performed, at least inpart, by sensor processor 402, sampling rate engine 108 and rate controlgenerator 110.

In at least some embodiments, it may be desirable to maintain a reducedsampling rate for a predetermined or preselected time interval. Anexample of such use has been described above in conjunction with FIG. 6.If such a time mode is provided, block 806 proceeds by the “Yes” branchto block 808. Otherwise, block 806 proceeds to block 812, describedbelow, via the “No” branch. Returning to decision block 808, it isdetermined if the aforementioned predetermined or preselected timeinterval is expired. If yes, decision block 808 proceeds to block 810and the sampling rate is restored to its pre-reduction value andaccelerometer rate process 517 returns to block 512. Otherwise, if thetime interval has not expired, decision block 808 proceeds to block 812.

In at least some embodiments, blocks 812 and 814 provide for calibrationof an accelerometer during instances of low user dynamics. In someembodiments, it may be desirable to calibrate an accelerometer sensor atlow user dynamics. However, calibration may, in many, if not mostembodiments benefit from a high sampling rate. The high number ofsamples provided thereby increase calibration accuracy. Thus, in block812, if an accelerometer is being calibrated during an instance of lowuser dynamics, via the “T” branch, the accelerometer sampling rate isrestored to a high value in block 814, and the high sampling rate ismaintained while the calibration proceeds via the loop in decision block812. In at least some embodiments, calibration may proceed until userdynamics are no longer low. When calibration is complete(WHILE(calibrate)=FALSE), decision block 812 returns to block 512 by the“F” branch. If upon return to block, user dynamics are low, block 512proceeds via the “F” branch back to block 517 where the accelerometersampling rate may be reduced, reflecting the low user dynamics viablocks 802 and 804.

As described above, an application may override the reduction of asensor sampling rate that might otherwise be instantiated based on theuser dynamics. Consider, as an example, yaw which for the purpose of thedisclosure is the difference or deviation of the user's direction ofmotion relative to the direction the user device is pointing. Thus, thedirection the user is heading may be determined by adjusting the headingof the user's device by the yaw angle. Typically, once initialized, yawmay be tracked at a reduced sampling rate inasmuch as yaw generallyvaries slowly. During initialization of the yaw, however, it may bedesirable to maintain sensor sampling rates at a high value. Anexemplary yaw tracking process 900 in accordance with at least someembodiments is shown in FIG. 9. Yaw tracking process 900 may beperformed, at least in part by sensor processor 402, for example assoftware or firmware running thereon, or alternatively on a generalpurpose CPU, or in yet another alternative, as application specifichardware implemented in sensor processor 402 or, in still anotheralternative, as a discrete ASIC.

If the yaw is being initialized, block, 902, process 900 may, in block904, override the gyro sampling rate as otherwise may be determinedbased on user dynamics as described above in conjunction with FIG. 5. Inthis way, a high gyro sampling rate may be maintained for the purpose ofinitializing the yaw value. In block 906, process 900 loops while theinitial value of the yaw is computed in block 908. The initial value ofthe yaw may be determined using a magnetic heading from the magnetometerdata and a coarse location determination. As would be recognized bythose skilled in the art, a coarse location may be obtained, forexample, in a smartphone embodiment by triangulation from a plurality ofcell towers. Alternatively, embodiments equipped with GPS may obtainlocation data therefrom. Using the location data, a magnetic declinationangle for the particular location may be computed using algorithms knownin the art. The yaw may then be determined using magnetometer datacorrected for the declination of the Earth's magnetic field at thelocation of the end-user device. Upon completion of the initialization,process 900 breaks out of the loop in block 906 and releases the gyrosampling rate override in block 910.

If, in block 902, the yaw is not being initialized (or upon completionof the initialization and release of the gyro sampling rate at block810), the yaw sampling rate is set in blocks 912 and 914. If, in block912, the gyro and accelerometer sampling rates are low, as for example,determined by low user dynamics in accordance with the principles of thedisclosure, block 912 proceeds by the “No” branch and a new yaw value iscomputed, block 916. A new yaw value may be computed by using theinitial or previously determined yaw and an integration of gyro andaccelerometer data to determine a current angular position offset froman initial or prior angular position. Briefly, in at least someembodiments, such as a hand-held device as describe in the in therelated U.S. patent application Ser. No. 13/023,987 for“ACCELEROMETER-AIDED GYROSCOPE” referenced above, the orientation of thedevice with respect to ground can vary depending on how the user isholding the device. In such embodiments, the updated angular positionsfrom the gyro measurements may be used to transform the magnetometerdata, which is in the magnetometer sensor's reference frame into a localEast, North, Up (ENU) frame. This so-called de-tilted magnetometer datamay then be used to compute the yaw angle. If m_(x) and m_(y) denote thede-tilted magnetometer data in the “E” and “N” directions respectively,then the yaw angle, y, may be determined by:

y=arctan(m _(x) /m _(y))−d  (1)

where d denotes the declination angle at the location of the end userdevice. Suitable techniques for the numerical integration of data areknown in the art, and it would be appreciated by those skilled in theart that such techniques are themselves based on discrete data samples.Returning to block 912, if either the gyro sampling rate, accelerometersampling rate or both are high, for example if user dynamics is high,process 900 may, in block 914, process a subset of the gyro datasamples. Recall that, although user dynamics might be high, the yaw, inat least some embodiments, may be slowly varying and yaw tracking maynot need to use all of the gyro samples generated at the high gyrosampling rate. In other words, process 900 at block 914 subsamples thegyro data and, in block 916 computes the new yaw value based on thesubsampled gyro and/or accelerometer data.

Similarly to the yaw, heading values may, in at least some embodiments,be tracked at reduced sampling rate. Refer now to FIG. 10 illustrating aprocess 1000 for heading tracking in accordance with at least someembodiments of the disclosure. Heading tracking process 1000 may beperformed, at least in part by sensor processor 402, for example assoftware or firmware running thereon, or alternatively on a generalpurpose CPU, or in yet another alternative, as application specifichardware implemented in sensor processor 402 or, in still anotheralternative, as a discrete ASIC. Heading tracking may use data from acombination of the magnetometer, gyro and accelerometer.

If the heading is being initialized, block, 1002, process 1000 may, inblock 1004, override the magnetometer and/or gyro sampling rates asotherwise may be determined based on user dynamics as described above inconjunction with FIG. 5. (Recall, that in accordance with at least someembodiments, the sampling rates of some sensors may be reduced while thesampling rates of other sensors may remain high.) In this way, highmagnetometer and gyro sampling rates may be maintained for the purposeof initializing the heading value. In block 1006, process 1000 loopswhile the initial value of the heading is computed in block 1008. Theinitial value of the heading may be determined using magnetometer data.Upon completion of the initialization, process 1000 breaks out of theloop in block 1006 and releases the magnetometer sampling rate and/orgyro sampling rate override in block 1010.

If, in block 1002, the heading is not being initialized (or uponcompletion of the initialization and release of the gyro sampling rateat block 1010), the heading sampling rate is set in blocks 1012 and1014. If, in block 1012, the gyro sampling rate and accelerometersampling rate are low, as for example, determined by low user dynamicsin accordance with the principles of the disclosure, block 1012 proceedsby the “No” branch and a new heading value is computed, block 1016. Anew heading value may be computed by using the initial or previouslydetermined heading and an integration of gyro data and accelerometerdata to determine a current heading offset from an initial or priorheading. Techniques for the numerical integration of data are known inthe art, and it would be appreciated by those skilled in the art thatsuch techniques are themselves based on discrete data samples. Returningto block 1012, if either the accelerometer or gyro sampling rate, orboth are high, for example if user dynamics is high, process 1000 may,in block 1014, process a subset of the accelerometer and/or gyro datathat has been sampled at the high rate. Recall that, although userdynamics might be high, the heading, in at least some embodiments, maybe slowly varying and heading tracking may not need to use all of thegyro and accelerometer samples generated at the high sampling rate. Inother words, process 1000 at block 1014 subsamples the gyro and/oraccelerometer data and, in block 1016 computes the new heading valuebased on the subsampled gyro and/or accelerometer data.

Returning to FIG. 5, in at least some embodiments, it may be desirableto adjust the altimeter sampling rate, block 522. (The altimeter mayequivalently be referred to a barometer or a pressure sensor.) Forexample, if high user dynamics in the vertical direction are detected bythe accelerometer, it may be desirable to increase the altimetersampling rate over a lower, perhaps default, value. Thus, by way offurther example, to track the floor that an elevator car is on, it maybe desirable to sample the altimeter at a high sampling rate. A processfor adaptively adjusting an altimeter sampling rate, block 522, is shownin further detail in FIG. 11. As would be understood by those skilled inthe art, in at least some embodiments, all of portions of block 522 maybe implemented in hardware, such as an application specific integratedcircuit (ASIC) or in at least some alternative embodiments as softwareor firmware running on, for example, sensor processor 402 or a generalpurpose central processing unit (CPU) (not shown) in the deviceemploying the plurality of sensors 102. If high vertical dynamics aresensed, process 522 loops via “WHILE” block 1102, and in block 1104sets, via the “T” branch of block 1102, a sampling rate of the altimeterto a high value, which may be, for example, selected from apredetermined set of values as described above in conjunction withFIG. 1. Upon the return of vertical dynamics to a low value, process 522breaks out of the “WHILE” loop via the “F” branch of block 1102. Inblock 1106 the sampling rate of the altimeter is restored to a lowvalue, which may, in at least some embodiments be a default value.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present invention. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

What is claimed is:
 1. A method comprising: detecting if user dynamicsare low indicated using an output of a first MEMS sensor; if userdynamics are low: determining a first sensor sampling rate valuecorresponding to the low user dynamics, wherein the first sensorsampling rate value is less than a second sensor sampling rate valuecorresponding to high user dynamics; and adjusting a sampling rate of asecond MEMS sensor to the first sensor sampling rate value.
 2. Themethod of claim 2 wherein the first MEMS sensor is different than thesecond MEMS sensor.
 3. The method of claim 1 further comprising:calibrating the second MEMS sensor; and restoring the first samplingrate value to the second sampling rate value during the calibrating. 4.The method of claim 3 further comprising: after the calibrating,determining if user dynamics are low; and if user dynamics are low,adjusting the sampling rate of the second MEMS sensor to the firstsampling rate value.
 5. The method of claim 1 further comprisingdisabling calibration of a third MEMS sensor in response to detectinglow user dynamics.
 6. The method of claim 1 wherein the second MEMSsensor comprises a first ADC and a second ADC, and wherein adjusting thesampling rate of the second MEMS sensor comprises selecting one of thefirst and second ADCs having the second sampling rate value.
 7. Themethod of claim 1 wherein the second MEMS sensor comprises an ADC havinga programmable sampling rate comprising an n-bit binary value andwherein adjusting the sampling rate of the second MEMS sensor comprisessetting a programming value of the programmable sampling rate in which mleast significant bits of the programming value are negated, and whereinm comprises a number of bits less than n.
 8. The method of claim 1further comprising determining a physical parameter selected from thegroup consisting of a yaw and a heading, the determining of the physicalparameter comprising: initializing a value of the physical parameter;and if user dynamics are high, integrating a subset of samples, whereinthe samples are obtained at the second sampling rate, to generate a newvalue of the physical parameter.
 9. A system comprising: a plurality ofsensors; a first logic unit coupled to an output of at least one sensorof the plurality of sensors, the first logic unit estimating userdynamics in response to an output signal from the at least one sensor;second logic unit coupled to the first logic unit, the second logic unitgenerating a sampling rate value of a first MEMS sensor in response toan output signal from the first logic unit, and wherein the first MEMSsensor comprises a selectable sampling rate and wherein the selectablesampling rate is selected in response to the generated sampling ratevalue.
 10. The system of claim 9 further comprising a sensor processor,the sensor processor comprising the first logic unit and the secondlogic unit.
 11. The system of claim 9 wherein: the second logic unitcomprises a sampling rate engine and a rate control generator, thesampling rate engine configured to receive the output signal from thefirst logic unit, and the rate control generator coupled to the samplingrate engine and generating an output signal in response to the samplingrate value, and wherein the rate control generator output signal iscoupled to the first MEMS sensor and configured to select a samplingrate of the first MEMS sensor.
 12. The system of claim 10 wherein thesensor processor is configured to estimate a physical parameter usingsensor data from at least two sensors of the plurality of sensors. 13.The system of claim 9 wherein the first sensor comprises a first ADC anda second ADC, and wherein selecting a sampling rate of the first sensorcomprises selecting one of the first and second ADCs having the samplingrate value.
 14. The system of claim 9 wherein the first sensor comprisesan ADC, the ADC comprising a programmable sampling rate, and wherein thefirst sensor selectable sampling rate is selected in response to aprogramming value received from the second logic unit.
 15. The system ofclaim 14 wherein a programming value of the programmable sampling ratecomprises an n-bit binary value and wherein m least significant bits ofthe programming value received from the second logic unit are negated,and wherein m comprises a number of bits less than n.
 16. The system ofclaim 12 wherein the physical parameter is selected from the groupconsisting of: a yaw; and a heading.
 17. A system comprising: aplurality of sensors; a sensor processor coupled to an output of eachsensor of the plurality of sensors, the sensor processor estimating userdynamics in response to a first output signal of a first sensor of theplurality of sensors; a sampling rate engine coupled to an output of thesensor processor, the sampling rate engine determining a sampling ratevalue of a second sensor of the plurality of sensors in response to auser dynamics value from the sensor processor; and wherein the secondsensor comprises a selectable sampling rate, the selectable samplingrate configured in response to the sampling rate value determined by thesampling rate engine.
 18. The system of claim 17 further comprising arate control generator configured to receive the sampling rate valuedetermined by the sampling rate engine and coupled to the second sensor,wherein the selectable sampling rate is selected in response to anoutput of the rate control generator, the output of the rate controlgenerator determined in response to the sampling rate value.
 19. Thesystem of claim 18 wherein the second sensor comprises a first ADC and asecond ADC, and wherein the selectable sampling rate comprises one of asampling rate of the first ADC and a sampling rate of the second ADC.20. The system of claim 17 wherein the sensor processor is configured toestimate a physical parameter using sensor data from at least twosensors of the plurality of sensors.